GVT Academy offers a comprehensive Data Science Course designed for freshers, graduates, and working professionals who want to build a successful career in Data Science, Machine Learning, Artificial Intelligence, and Analytics. This job-oriented Data Science Training Program covers everything from data analysis and visualization to advanced machine learning, deep learning, NLP, and cloud technologies. This Beginner to Advanced Data Science Course is delivered in Online + Offline Hybrid Mode with a duration of 5–6 months, focusing on hands-on practical learning, real-world projects, and career readiness. Why Choose This Data Science Training Program? This course is ideal for learners searching for: Data Science Course with Python Machine Learning Course with Live Projects Data Science Certification Program AI & Deep Learning Training Data Analyst to Data Scientist Career Path Data Science Course with Placement Support Tools & Technologies Covered Learners gain expertise in Python for Data Science, Excel & Power BI for analytics, SQL & NoSQL databases, Machine Learning algorithms, Deep Learning with TensorFlow & Keras, Natural Language Processing (NLP), and Big Data & Cloud technologies. Key Learning Modules Data Science Fundamentals & Statistics Python Programming, NumPy, Pandas, Matplotlib Exploratory Data Analysis & Feature Engineering SQL & MongoDB for Data Science Machine Learning (Regression, Classification, Clustering) Deep Learning, Neural Networks & AI NLP, Text Analytics & Transformers Big Data, Spark & Cloud Deployment Capstone Project & Portfolio Development Learners work on mini-projects, live case studies, and a full end-to-end capstone project, gaining exposure to real business and industry datasets. Career Support & Certification GVT Certified Data Scientist Certification Resume & LinkedIn Profile Optimization Mock Interviews & Technical Interview Preparation Career guidance for Data Scientist, ML Engineer & Data Analyst roles Guidance for global certifications (Google, Microsoft, IBM) Learning Outcomes After completing this professional Data Science Course, learners will be able to: Build, evaluate, and deploy ML & AI models Perform data-driven decision making Visualize and communicate insights effectively Work confidently on real-world data science projects If you are looking for the Best Data Science Course with Python, Machine Learning, AI & Live Projects, GVT Academy provides the skills, certification, and career support needed to succeed in today’s data-driven job market.

1. GVT Academy – Data Science Course
Curriculum
Duration: 5–6 Months | Mode: Online + Offline Hybrid | Level: Beginner to Advanced
Course Objective
To equip students with in-demand data science skills including programming, data analysis,
machine learning, and real-world problem-solving to pursue roles such as Data Analyst,
Machine Learning Engineer, and Data Scientist.
Course Structure Overview
Module No. Module Title
01 Introduction to Data Science
02
Excel Role in Data science
03 Power BI Role in Data science
04 Python Programming for Data Science
05 Python Basics
06 Python Packages
07 Importing Data
08 Statistics Basics
09 Data Analysis & Visualization
10 Database Management & SQL
11 Non- Relational Database Management &
No-SQL for data science
12 Machine Learning Essentials

2. 13 Deep Learning & AI
14 Natural Language Processing (NLP)
15 Big Data & Cloud Technologies
16 Capstone Project + Portfolio
17 Career Support & Certification
Module Breakdown
Data science with python
Module 1: Introduction to Data Science
● Selecting Rows / observations
● Rounding Number
● Selecting Columns
● Merging data & Aggregation
Module 2: Excel Role in Data science
● Understanding Data cleaning & Preparation in Excel
● Understanding Data analysis with Various excel functions
● Report developed for Pivot
● Data visualization with charts
● Data Modeling
Module 3: Power BI Role in Data science
● Power Bi Introduction
● Installation of Power BI
● Handle Large Dataset in Power BI

3. ● Data Preprocess while load into model
● Understand DAX for Data analysis
● Create Visualization develop dashboard
Module 4: Python Programming for Data Science
● What is Python?
● Installing Python
● Python IDE,
Module 5: Python Basics
● Python Data Types
● Indexing, Slicing
● If Statements
● Loops
● Functions
● Tuples
● List
● Array
● File Handling
Module 6: Python Packages:
● Python Libraries: How to import
● NumPy
● Pandas
● Matplotlib

4. ● Seaborn
Module 7: Importing Data
● Reading CSV file
● Writing data to CSV file
● Handle CSV data with list, Arrays
Module 8: Statistics Basics
● Mean
● Median
● Mode
● Normal Distribution
● Probability Basics & Types
● Variance & Standard Deviation
● Bias variance
● Missing value treatment
● Correlation
Module 9: Data Analysis & Visualization
● Exploratory Data Analysis (EDA)
● Data Cleaning & Transformation
● Feature Engineering Techniques
● Data Visualization with Pytthon
✅ Mini-project: Employee Recruitment report Analysis
Module 10: Database Management & SQL
● Relational Database & SQL (Sql Server)

5. ● DDL,DML,DCL
● SQL Joins
● SQL Subqueries
● SQL Aggregations
● SQL ETL Basics
● Views & Schemas
● Integration with Python & Data access
✅ Assignment: SQL Queries for HR Analytics
Module 11: Non- Relational Database Management & No-SQL for data
science
● Introduction of Unstructured Data & Non-relational Database
● NoSQL Overview
● Un-structured Data formats & handle it
● Data models
● Flexible Schema
● Understanding data in JSON documents
● MongoDB Database
✅ Mini-project: Statistical Study on HR Data (e.g., COVID or Inflation)
Module 12: Machine Learning Essentials
● ML Workflow
● Supervised Learning:
● Linear Regression

6. ● Logistic Regression, Decision Trees
● Unsupervised Learning: K-Means, PCA
● Model Evaluation (ROC, Precision, Recall, F1)
● Hyperparameter Tuning (Grid Search, Cross-Validation)
✅ Mini-project: Quality of hire predictions & Chatbots
Module 13: Deep Learning & AI
● Introduction to Neural Networks
● TensorFlow & Keras
● CNNs for Image Processing
● RNNs for Sequence Data
● Generative Models: Autoencoders, GANs
✅ Project: Employee Attrition Prediction Report
Module 14: Natural Language Processing (NLP)
● Text Cleaning, Tokenization
● TF-IDF, Bag of Words
● Named Entity Recognition
● Sentiment Analysis
● Using Transformers (BERT or GPT)
✅ Mini-project: Automated Resume Parsing & Bias Detection for HR
Module 15: Cloud Technologies
● Data Lakes vs Data Warehouses
● Spark & Azure Basics

7. ● Deploying ML Models on Cloud
● GitHub
✅ Mini-project: Big Data Pipeline with Spark
Module 16: Capstone Project + Portfolio
● Choose a real-world domain: HR, Healthcare, Finance, E-commerce
● Full Data Science Lifecycle: Data Collection, Cleaning, Modeling, Visualization, and
Presentation
● Peer Reviews & Faculty Feedback
🎓 Capstone Examples:
● Payroll, leave, and benefits management
● Workforce Dashboard with survey Analysis
Module 17: Career Support & Certification
● Resume & LinkedIn Optimization
● Technical Interview Prep (DS/ML/SQL/Case Studies)
● Mock Interviews (1-on-1)
● Freelancing & Job Portals Guidance
● Certification: GVT Certified Data Scientist
🎖 Bonus: Guidance on External Certifications
● Google Data Analytics
● Microsoft Certified: Azure AI Engineer
● IBM Data Science Professional Certificate
Tools & Platforms Covered

8. ● Languages: Python, SQL
● Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras, NLTK
● Visualization: Power BI, Excel
● Databases: SQL Server, MongoDB
● Cloud Data storage: Azure, Github
Elective Tracks (Optional Add-ons)
1. Data Science for Business (with Excel & Power BI)
2. AI for Finance (Time Series Forecasting)
3. AI in Healthcare (Medical Imaging + NLP)
4. Computer Vision Advanced Projects
Prerequisites
● No coding background required (Beginner-friendly track available)
● Basic math understanding is helpful
● Laptop/Desktop with internet access
Learning Outcomes
After successful completion, students will be able to:
● Build and deploy end-to-end ML and AI models
● Extract insights from complex datasets
● Visualize business-critical information

9. ● Apply for Data Scientist, ML Engineer, or Analyst roles confidently
● Communicate results to technical and non-technical stakeholders